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Performance enhancement of traffic information gathering (PEnTInG) algorithm for vehicular ad‐hoc networks
Author(s) -
Kumar Rajeev,
Kumar Dilip,
Kumar Dinesh
Publication year - 2019
Publication title -
international journal of communication systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.344
H-Index - 49
eISSN - 1099-1131
pISSN - 1074-5351
DOI - 10.1002/dac.4111
Subject(s) - computer science , vehicular ad hoc network , dissemination , wireless ad hoc network , computer network , cluster (spacecraft) , context (archaeology) , computer security , telecommunications , wireless , paleontology , biology
Summary Vehicular ad‐hoc networks (VANETs) play a vital role in today's context of vehicular traffic. In this paper, clusters of vehicles are created on the basis of average speed of the vehicles. One cluster communicates with the next cluster through a cluster head and also share the same information with next cluster heads and installed road side units (RSUs). By using this technique, we can solve the problem of rough driving behavior and road terrorism which is due to speed variation of vehicles and fake information dissemination by the drivers. Many a times, drivers may spread fake accident‐related information into the network which is a serious cause of concern in VANETs. It is ensured that such drivers are not allowed to spread wrong information in the network to avoid accidents. To solve this problem, we developed performance enhancement of traffic information gathering (PEnTInG) algorithm that selects only those drivers/vehicles as cluster heads in a cluster who has maximum value of the cluster head factor (CHF). The CHF is derived by considering different weights in range of 0 to 1 of relative average speed, time to leave, trust factor, and neighborhood degree. Further, the elected cluster head shares and stores the same information with the RSUs. In case, a driver wants to disseminate fake or wrong information in a network, then that vehicle driver can be easily tracked by the local authority by accessing RSU data. Simulation results show that the stability of PEnTInG is increased by 25% against the existing schemes viz. lowest‐ID, MCMF, and cluster‐based technique.